






"""simple docstring"""





import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


_SCREAMING_SNAKE_CASE   :							Dict          =		trt.Logger(trt.Logger.WARNING)
_SCREAMING_SNAKE_CASE   :							Tuple          =		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_SCREAMING_SNAKE_CASE   :							str          =		logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE   :							Any          =		argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

_SCREAMING_SNAKE_CASE   :							str          =		parser.parse_args()

if args.tokenizer_name:
  _SCREAMING_SNAKE_CASE   :							Tuple          =		AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
  raise ValueError(
      '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
      '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
  )

logger.info('''Training/evaluation parameters %s''', args)

_SCREAMING_SNAKE_CASE   :							Dict          =		args.per_device_eval_batch_size

_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_SCREAMING_SNAKE_CASE   :							List[Any]          =		True

_SCREAMING_SNAKE_CASE   :							Optional[int]          =		'''temp_engine/bert-fp32.engine'''
if args.fpaa:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		'''temp_engine/bert-fp16.engine'''
if args.inta:
  _SCREAMING_SNAKE_CASE   :							int          =		'''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
  os.makedirs('''temp_engine''')

_SCREAMING_SNAKE_CASE   :							str          =		1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
  with open(args.onnx_model_path, '''rb''') as model:
    if not parser.parse(model.read()):
      for error in range(parser.num_errors):
        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
  _SCREAMING_SNAKE_CASE   :							int          =		[network.get_input(i) for i in range(network.num_inputs)]
  _SCREAMING_SNAKE_CASE   :							int          =		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

  with builder.create_builder_config() as config:
    _SCREAMING_SNAKE_CASE   :							str          =		1 << 50
    if STRICT_TYPES:
      config.set_flag(trt.BuilderFlag.STRICT_TYPES)
    if args.fpaa:
      config.set_flag(trt.BuilderFlag.FPaa)
    if args.inta:
      config.set_flag(trt.BuilderFlag.INTa)
    _SCREAMING_SNAKE_CASE   :							int          =		builder.create_optimization_profile()
    config.add_optimization_profile(profile)
    for i in range(len(input_names)):
      profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
    _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		builder.build_engine(network, config)

    # serialize_engine and store in file (can be directly loaded and deserialized):
    with open(engine_name, '''wb''') as f:
      f.write(engine.serialize())







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Any						,							_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						Union[str, Any]					) ->							Optional[Any]:
  lowerCamelCase_       	=		np.asarray(inputs['input_ids']						,							dtype=np.intaa					)
  lowerCamelCase_       	=		np.asarray(inputs['attention_mask']						,							dtype=np.intaa					)
  lowerCamelCase_       	=		np.asarray(inputs['token_type_ids']						,							dtype=np.intaa					)

  # Copy inputs
  cuda.memcpy_htod_async(d_inputs[0]						,							input_ids.ravel()						,							_lowerCamelCase					)
  cuda.memcpy_htod_async(d_inputs[1]						,							attention_mask.ravel()						,							_lowerCamelCase					)
  cuda.memcpy_htod_async(d_inputs[2]						,							token_type_ids.ravel()						,							_lowerCamelCase					)
  # start time
  lowerCamelCase_       	=		time.time()
  # Run inference
  context.execute_async(
      bindings=[int(_lowerCamelCase					) for d_inp in d_inputs] + [int(_lowerCamelCase					), int(_lowerCamelCase					)]						,							stream_handle=stream.handle					)
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  cuda.memcpy_dtoh_async(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  # Synchronize the stream and take time
  stream.synchronize()
  # end time
  lowerCamelCase_       	=		time.time()
  lowerCamelCase_       	=		end_time - start_time
  lowerCamelCase_       	=		(h_outputa, h_outputa)
  # print(outputs)
  return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_SCREAMING_SNAKE_CASE   :							int          =		Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
  datasets.utils.logging.set_verbosity_warning()
  transformers.utils.logging.set_verbosity_info()
else:
  datasets.utils.logging.set_verbosity_error()
  transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
  set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		load_dataset(args.dataset_name, args.dataset_config_name)
else:
  raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

_SCREAMING_SNAKE_CASE   :							Tuple          =		raw_datasets['''validation'''].column_names

_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		'''question''' if '''question''' in column_names else column_names[0]
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		'''context''' if '''context''' in column_names else column_names[1]
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
  logger.warning(
      F'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
      F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
  )

_SCREAMING_SNAKE_CASE   :							Tuple          =		min(args.max_seq_length, tokenizer.model_max_length)







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]					) ->							Tuple:
  lowerCamelCase_       	=		[q.lstrip() for q in examples[question_column_name]]

  # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
  # in one example possible giving several features when a context is long, each of those features having a
  # context that overlaps a bit the context of the previous feature.
  lowerCamelCase_       	=		tokenizer(
      examples[question_column_name if pad_on_right else context_column_name]						,							examples[context_column_name if pad_on_right else question_column_name]						,							truncation='only_second' if pad_on_right else 'only_first'						,							max_length=_lowerCamelCase						,							stride=args.doc_stride						,							return_overflowing_tokens=_lowerCamelCase						,							return_offsets_mapping=_lowerCamelCase						,							padding='max_length'						,							)

  # Since one example might give us several features if it has a long context, we need a map from a feature to
  # its corresponding example. This key gives us just that.
  lowerCamelCase_       	=		tokenized_examples.pop('overflow_to_sample_mapping'					)

  # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
  # corresponding example_id and we will store the offset mappings.
  lowerCamelCase_       	=		[]

  for i in range(len(tokenized_examples['input_ids']					)					):
    # Grab the sequence corresponding to that example (to know what is the context and what is the question).
    lowerCamelCase_       	=		tokenized_examples.sequence_ids(_lowerCamelCase					)
    lowerCamelCase_       	=		1 if pad_on_right else 0

    # One example can give several spans, this is the index of the example containing this span of text.
    lowerCamelCase_       	=		sample_mapping[i]
    tokenized_examples["example_id"].append(examples['id'][sample_index]					)

    # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
    # position is part of the context or not.
    lowerCamelCase_       	=		[
        (o if sequence_ids[k] == context_index else None)
        for k, o in enumerate(tokenized_examples['offset_mapping'][i]					)
    ]

  return tokenized_examples


_SCREAMING_SNAKE_CASE   :							int          =		raw_datasets['''validation''']
# Validation Feature Creation
_SCREAMING_SNAKE_CASE   :							List[str]          =		eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

_SCREAMING_SNAKE_CASE   :							List[Any]          =		default_data_collator

_SCREAMING_SNAKE_CASE   :							List[Any]          =		eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_SCREAMING_SNAKE_CASE   :							int          =		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						List[Any]="eval"					) ->							List[str]:
  lowerCamelCase_       	=		postprocess_qa_predictions(
      examples=_lowerCamelCase						,							features=_lowerCamelCase						,							predictions=_lowerCamelCase						,							version_2_with_negative=args.version_2_with_negative						,							n_best_size=args.n_best_size						,							max_answer_length=args.max_answer_length						,							null_score_diff_threshold=args.null_score_diff_threshold						,							output_dir=args.output_dir						,							prefix=_lowerCamelCase						,							)
  # Format the result to the format the metric expects.
  if args.version_2_with_negative:
    lowerCamelCase_       	=		[
        {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
    ]
  else:
    lowerCamelCase_       	=		[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

  lowerCamelCase_       	=		[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=_lowerCamelCase						,							label_ids=_lowerCamelCase					)


_SCREAMING_SNAKE_CASE   :							Any          =		load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
  # setup for TRT inferrence
  for i in range(len(input_names)):
    context.set_binding_shape(i, INPUT_SHAPE)
  assert context.all_binding_shapes_specified







  def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							Tuple:
    return trt.volume(engine.get_binding_shape(_lowerCamelCase					)					) * engine.get_binding_dtype(_lowerCamelCase					).itemsize

  # Allocate device memory for inputs and outputs.
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

  # Allocate output buffer
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
  _SCREAMING_SNAKE_CASE   :							Any          =		cuda.mem_alloc(h_outputa.nbytes)
  _SCREAMING_SNAKE_CASE   :							str          =		cuda.mem_alloc(h_outputa.nbytes)

  # Create a stream in which to copy inputs/outputs and run inference.
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		cuda.Stream()

  # Evaluation
  logger.info('''***** Running Evaluation *****''')
  logger.info(F'''  Num examples = {len(eval_dataset)}''')
  logger.info(F'''  Batch size = {args.per_device_eval_batch_size}''')

  _SCREAMING_SNAKE_CASE   :							Any          =		0.0
  _SCREAMING_SNAKE_CASE   :							Dict          =		0
  _SCREAMING_SNAKE_CASE   :							Any          =		timeit.default_timer()

  _SCREAMING_SNAKE_CASE   :							int          =		None
  for step, batch in enumerate(eval_dataloader):
    _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							List[Any]          =		model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
    total_time += infer_time
    niter += 1

    _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		outputs
    _SCREAMING_SNAKE_CASE   :							Any          =		torch.tensor(start_logits)
    _SCREAMING_SNAKE_CASE   :							Dict          =		torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
    _SCREAMING_SNAKE_CASE   :							Any          =		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

    _SCREAMING_SNAKE_CASE   :							List[str]          =		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    _SCREAMING_SNAKE_CASE   :							Dict          =		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

  if all_preds is not None:
    _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		nested_truncate(all_preds, len(eval_dataset))

  _SCREAMING_SNAKE_CASE   :							List[Any]          =		timeit.default_timer() - start_time
  logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
  # Inference time from TRT
  logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
  logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))






  logger.info('''Total Number of Inference =  %d''', niter)

_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		post_processing_function(eval_examples, eval_dataset, all_preds)
_SCREAMING_SNAKE_CASE   :							Dict          =		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






logger.info(F'''Evaluation metrics: {eval_metric}''')






"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
					from huggingface_hub import snapshot_download
					from pyctcdecode import BeamSearchDecoderCTC

					from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
					from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
					from transformers import WavaVecaForCTC



@require_pyctcdecode
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
				lowerCAmelCase												=  dict(zip(SCREAMING_SNAKE_CASE_      ,range(len(SCREAMING_SNAKE_CASE_			)			)			)			)

				lowerCAmelCase												=  {
				    '''unk_token''': '''<unk>''',
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				}
				lowerCAmelCase												=  {
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16_000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}

				lowerCAmelCase												=  tempfile.mkdtemp()
				lowerCAmelCase												=  os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['''vocab_file''']			)
				lowerCAmelCase												=  os.path.join(self.tmpdirname      ,SCREAMING_SNAKE_CASE_			)
				with open(self.vocab_file      ,'''w'''      ,encoding='''utf-8'''			) as fp:
					fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)

				with open(self.feature_extraction_file      ,'''w'''      ,encoding='''utf-8'''			) as fp:
					fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)

				# load decoder from hub
				lowerCAmelCase												=  '''hf-internal-testing/ngram-beam-search-decoder'''






			def 					UpperCAmelCase							(self  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.add_kwargs_tokens_map.copy()
				kwargs.update(SCREAMING_SNAKE_CASE_			)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Optional[int]:





				"""simple docstring"""


				return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				shutil.rmtree(self.tmpdirname			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				processor.save_pretrained(self.tmpdirname			)
				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname			)

				# tokenizer
				self.assertEqual(processor.tokenizer.get_vocab()      ,tokenizer.get_vocab()			)
				self.assertIsInstance(processor.tokenizer      ,SCREAMING_SNAKE_CASE_			)

				# feature extractor
				self.assertEqual(processor.feature_extractor.to_json_string()      ,feature_extractor.to_json_string()			)
				self.assertIsInstance(processor.feature_extractor      ,SCREAMING_SNAKE_CASE_			)

				# decoder
				self.assertEqual(processor.decoder._alphabet.labels      ,decoder._alphabet.labels			)
				self.assertEqual(
				    processor.decoder.model_container[decoder._model_key]._unigram_set      ,decoder.model_container[decoder._model_key]._unigram_set      ,)
				self.assertIsInstance(processor.decoder      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM(
				    tokenizer=self.get_tokenizer()      ,feature_extractor=self.get_feature_extractor()      ,decoder=self.get_decoder()			)
				processor.save_pretrained(self.tmpdirname			)

				# make sure that error is thrown when decoder alphabet doesn't match
				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(
				    self.tmpdirname      ,alpha=5.0      ,beta=3.0      ,score_boundary=-7.0      ,unk_score_offset=3			)

				# decoder
				self.assertEqual(processor.language_model.alpha      ,5.0			)
				self.assertEqual(processor.language_model.beta      ,3.0			)
				self.assertEqual(processor.language_model.score_boundary      ,-7.0			)
				self.assertEqual(processor.language_model.unk_score_offset      ,3			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_tokenizer()
				# add token to trigger raise
				tokenizer.add_tokens(['''xx''']			)
				with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_      ,'''include'''			):
					WavaVecaProcessorWithLM(
					    tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=self.get_feature_extractor()      ,decoder=self.get_decoder()			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  floats_list((3, 1_000)			)

				lowerCAmelCase												=  feature_extractor(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)
				lowerCAmelCase												=  processor(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()      ,input_processor[key].sum()      ,delta=1e-2			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''This is a test string'''

				lowerCAmelCase												=  processor(text=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer(SCREAMING_SNAKE_CASE_			)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]      ,encoded_processor[key]			)






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=(2, 10, 16)      ,SCREAMING_SNAKE_CASE_  :	List[Any]=77			)       ->	Optional[int]:





				"""simple docstring"""


				np.random.seed(SCREAMING_SNAKE_CASE_			)
				return np.random.rand(*SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits(shape=(10, 16)      ,seed=13			)

				lowerCAmelCase												=  processor.decode(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  decoder.decode_beams(SCREAMING_SNAKE_CASE_			)[0]

				self.assertEqual(decoded_decoder[0]      ,decoded_processor.text			)
				self.assertEqual('''</s> <s> </s>'''      ,decoded_processor.text			)
				self.assertEqual(decoded_decoder[-2]      ,decoded_processor.logit_score			)
				self.assertEqual(decoded_decoder[-1]      ,decoded_processor.lm_score			)






			@parameterized.expand([[None], ['''fork'''], ['''spawn''']]			)
			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
				#       otherwise, the LM won't be available to the pool's sub-processes.
				# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
				if pool_context is None:
					lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_			)
				else:
					with get_context(SCREAMING_SNAKE_CASE_			).Pool() as pool:
						lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				with get_context('''fork'''			).Pool() as p:
					lowerCAmelCase												=  decoder.decode_beams_batch(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  [], [], []
				for beams in decoded_beams:
					texts_decoder.append(beams[0][0]			)
					logit_scores_decoder.append(beams[0][-2]			)
					lm_scores_decoder.append(beams[0][-1]			)

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.text			)
				self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']      ,decoded_processor.text			)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.logit_score			)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.lm_score			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  15
				lowerCAmelCase												=  -20.0
				lowerCAmelCase												=  -4.0

				lowerCAmelCase												=  processor.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,beam_width=SCREAMING_SNAKE_CASE_      ,beam_prune_logp=SCREAMING_SNAKE_CASE_      ,token_min_logp=SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  decoded_processor_out.text

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				with get_context('''fork'''			).Pool() as pool:
					lowerCAmelCase												=  decoder.decode_beams_batch(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,beam_width=SCREAMING_SNAKE_CASE_      ,beam_prune_logp=SCREAMING_SNAKE_CASE_      ,token_min_logp=SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  [d[0][0] for d in decoded_decoder_out]
				lowerCAmelCase												=  [d[0][2] for d in decoded_decoder_out]
				lowerCAmelCase												=  [d[0][3] for d in decoded_decoder_out]

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']      ,SCREAMING_SNAKE_CASE_			)

				self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_      ,decoded_processor_out.logit_score			)			)
				self.assertTrue(np.allclose([-20.0_54, -18.4_47]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)

				self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_      ,decoded_processor_out.lm_score			)			)
				self.assertTrue(np.allclose([-15.5_54, -13.94_74]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  2.0
				lowerCAmelCase												=  5.0
				lowerCAmelCase												=  -20.0
				lowerCAmelCase												=  True

				lowerCAmelCase												=  processor.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,alpha=SCREAMING_SNAKE_CASE_      ,beta=SCREAMING_SNAKE_CASE_      ,unk_score_offset=SCREAMING_SNAKE_CASE_      ,lm_score_boundary=SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  decoded_processor_out.text

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)
				decoder.reset_params(
				    alpha=SCREAMING_SNAKE_CASE_      ,beta=SCREAMING_SNAKE_CASE_      ,unk_score_offset=SCREAMING_SNAKE_CASE_      ,lm_score_boundary=SCREAMING_SNAKE_CASE_      ,)

				with get_context('''fork'''			).Pool() as pool:
					lowerCAmelCase												=  decoder.decode_beams_batch(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  [d[0][0] for d in decoded_decoder_out]

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				self.assertEqual(lm_model.alpha      ,2.0			)
				self.assertEqual(lm_model.beta      ,5.0			)
				self.assertEqual(lm_model.unk_score_offset      ,-20.0			)
				self.assertEqual(lm_model.score_boundary      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				lowerCAmelCase												=  Path(language_model._kenlm_model.path.decode('''utf-8'''			)			).parent.parent.absolute()

				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  ['''alphabet.json''', '''language_model''']

				downloaded_decoder_files.sort()
				expected_decoder_files.sort()

				# test that only decoder relevant files from
				# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
				# are downloaded and none of the rest (e.g. README.md, ...)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  snapshot_download('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				lowerCAmelCase												=  Path(language_model._kenlm_model.path.decode('''utf-8'''			)			).parent.parent.absolute()

				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)

				local_decoder_files.sort()
				expected_decoder_files.sort()

				# test that both decoder form hub and local files in cache are the same
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  floats_list((3, 1_000)			)

				lowerCAmelCase												=  processor_wavaveca(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)
				lowerCAmelCase												=  processor_auto(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)

				for key in input_wavaveca.keys():
					self.assertAlmostEqual(input_wavaveca[key].sum()      ,input_auto[key].sum()      ,delta=1e-2			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  processor_wavaveca.batch_decode(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  processor_auto.batch_decode(SCREAMING_SNAKE_CASE_			)

				self.assertListEqual(decoded_wavaveca.text      ,decoded_auto.text			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				self.assertListEqual(
				    processor.model_input_names      ,feature_extractor.model_input_names      ,msg='''`processor` and `feature_extractor` model input names do not match'''      ,)






			@staticmethod
			def 					UpperCAmelCase							(SCREAMING_SNAKE_CASE_  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  [d[key] for d in offsets]
				return retrieved_list






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  self._get_dummy_logits()[0]

				lowerCAmelCase												=  processor.decode(SCREAMING_SNAKE_CASE_      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)
				# check Wav2Vec2CTCTokenizerOutput keys for word
				self.assertEqual(len(outputs.keys()			)      ,4			)
				self.assertTrue('''text''' in outputs			)
				self.assertTrue('''word_offsets''' in outputs			)
				self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']      ,'''word'''			)			)      ,outputs.text			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''word'''			)      ,['''<s>''', '''<s>''', '''</s>''']			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''start_offset'''			)      ,[0, 2, 4]			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''end_offset'''			)      ,[1, 3, 5]			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)

				# check Wav2Vec2CTCTokenizerOutput keys for word
				self.assertEqual(len(outputs.keys()			)      ,4			)
				self.assertTrue('''text''' in outputs			)
				self.assertTrue('''word_offsets''' in outputs			)
				self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				self.assertListEqual(
				    [''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			) for o in outputs['''word_offsets''']]      ,outputs.text			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''word'''			)      ,['''<s>''', '''<s>''', '''</s>''']			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''start_offset'''			)      ,[0, 2, 4]			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''end_offset'''			)      ,[1, 3, 5]			)






			@slow
			@require_torch
			@require_torchaudio
			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Any:





				"""simple docstring"""


				import torch

				lowerCAmelCase												=  load_dataset('''common_voice'''      ,'''en'''      ,split='''train'''      ,streaming=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  ds.cast_column('''audio'''      ,datasets.Audio(sampling_rate=16_000			)			)
				lowerCAmelCase												=  iter(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  next(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''			)
				lowerCAmelCase												=  WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''			)

				# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
				lowerCAmelCase												=  processor(sample['''audio''']['''array''']      ,return_tensors='''pt'''			).input_values

				with torch.no_grad():
					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			).logits.cpu().numpy()

				lowerCAmelCase												=  processor.decode(logits[0]      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
				lowerCAmelCase												=  [
				    {
				        '''start_time''': d['''start_offset'''] * time_offset,
				        '''end_time''': d['''end_offset'''] * time_offset,
				        '''word''': d['''word'''],
				    }
				    for d in output['''word_offsets''']
				]

				lowerCAmelCase												=  '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

				# output words
				self.assertEqual(''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			)      ,SCREAMING_SNAKE_CASE_			)
				self.assertEqual(''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			)      ,output.text			)

				# output times
				lowerCAmelCase												=  torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''start_time'''			)			)
				lowerCAmelCase												=  torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''end_time'''			)			)

				# fmt: off
				lowerCAmelCase												=  torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]			)
				lowerCAmelCase												=  torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]			)
				# fmt: on

				self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=0.01			)			)
				self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=0.01			)			)


